Gacon, JulienZoufal, ChristaCarleo, GiuseppeWoerner, Stefan2021-11-202021-11-202021-11-202021-10-2010.22331/q-2021-10-20-567https://infoscience.epfl.ch/handle/20.500.14299/183101WOS:000711452800001The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary Time Evolution. Computing the full QFIM for a model with d parameters, however, is computation-ally expensive and generally requires O(d(2)) function evaluations. To remedy these increasing costs in high-dimensional parameter spaces, we propose using simultaneous perturbation stochastic approximation techniques to approximate the QFIM at a constant cost. We present the resulting algorithm and successfully apply it to prepare Hamiltonian ground states and train Variational Quantum Boltzmann Machines.Quantum Science & TechnologyPhysics, MultidisciplinaryPhysicstext::journal::journal article::research article